Chiari L, Cappello A, Lenzi D, Della Croce U
Biomedical Engineering Unit, Department of Electronics, Computer Science and Systems, University of Bologna, Viale Risorgimento 2, I-40136, Bologna, Italy.
Gait Posture. 2000 Dec;12(3):225-34. doi: 10.1016/s0966-6362(00)00086-2.
An improved characterization of the dynamics of postural sway can provide a better understanding about the functional organization of the postural control system as well as a more robust tool for postural pattern recognition. To this aim, a novel parameterization was applied to the stabilogram diffusion analysis formerly proposed by Collins and De Luca [Collins JJ, De Luca CJ. Open-loop and closed-loop control of posture: a random-walk analysis of center-of-pressure trajectories. Exp Brain Res 1993;95:308-18] that considered the act of maintaining posture as a stochastic process. The main purpose of the present technique was to overcome some drawbacks of the model presented by Collins and De Luca that may restrain its potential application in clinical practice. The approach uses a unique non-linear model to describe the center of pressure (COP) dynamics that reduces the number of parameters and decreases their intra-subject variability; consequently, fewer trials are required to perform reliable estimates of stochastic parameters and this is of particular importance for subjects that cannot afford many repeated measurements because of age or pathology. Four new statistical mechanics parameters (NSMP) were computed on the log-log stabilogram diffusion plots and their estimates were compared in terms of reliability and sensitivity to the visual conditions with: (1) a minimal set of four summary statistic scores (SSS); and (2) the six statistical mechanics parameters (SMP) proposed by Collins and De Luca. All four NSMP showed at least a fair-to-good reliability (intraclass correlation coefficient, ICC>0.49) while SMP (ICC>0.20) showed some poor reliability. A better overall reliability was also observed with respect to SSS. Moreover, only NSMP had a similar score for eyes open and eyes closed conditions. Three out of four NSMP were also significantly sensitive to eyes open or closed conditions (P<0.001) while only three out of six SMP were sensitive to operating conditions (P<0.01).
对姿势摆动动力学进行改进的特征描述，有助于更好地理解姿势控制系统的功能组织，以及为姿势模式识别提供更强大的工具。为此，一种新的参数化方法被应用于柯林斯和德卢卡之前提出的稳定图扩散分析[柯林斯JJ，德卢卡CJ。姿势的开环和闭环控制：压力中心轨迹的随机游走分析。实验脑研究1993；95：308 - 18]，该分析将维持姿势的行为视为一个随机过程。本技术的主要目的是克服柯林斯和德卢卡提出的模型的一些缺点，这些缺点可能会限制其在临床实践中的潜在应用。该方法使用独特的非线性模型来描述压力中心（COP）动力学，减少了参数数量并降低了参数在个体内的变异性；因此，进行随机参数的可靠估计所需的试验次数更少，这对于因年龄或病理原因无法承受多次重复测量的受试者尤为重要。在对数 - 对数稳定图扩散图上计算了四个新的统计力学参数（NSMP），并将它们的估计值在可靠性和对视觉条件的敏感性方面与以下两者进行比较：（1）一组最少的四个汇总统计分数（SSS）；（2）柯林斯和德卢卡提出的六个统计力学参数（SMP）。所有四个NSMP均显示出至少良好到优秀的可靠性（组内相关系数，ICC>0.49），而SMP（ICC>0.20）显示出一些较差的可靠性。相对于SSS，还观察到了更好的整体可靠性。此外，只有NSMP在睁眼和闭眼条件下具有相似的分数。四个NSMP中有三个对睁眼或闭眼条件也有显著敏感性（P<0.001），而六个SMP中只有三个对操作条件敏感（P<0.01）。
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